Correctover MCP Server
OfficialRelated Servers
Alternatives to Correctover MCP Server
- AlicenseAqualityBmaintenanceLets AI agents query, manage, and operate their LLM observability data directly from the conversation. Provides 87 tools for cost analysis, alerting, anomaly detection, and runtime control gates.87209 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables LLM evaluation and observability by uploading documents, building test sets, running RAG pipelines, and automatically scoring answers for groundedness, hallucination risk, retrieval quality, latency, and cost, with tools exposed to MCP-compatible clients.1MIT
- AlicenseNot gradedqualityDmaintenanceTracks MCP server behavior from agent-reported interactions. Provides trust scores, behavioral baselines, anomaly detection, and compliance audit exports for EU AI Act and Singapore IMDA frameworks.13 npm1MIT
- AlicenseNot gradedqualityAmaintenanceMCP server that integrates with VictoriaMetrics vmanomaly API to enable AI-assisted anomaly detection, model management, and configuration generation.9Apache 2.0
- AlicenseNot gradedqualityAmaintenanceMCP server that gives LLMs access to formal verification via Z3 and SWI-Prolog, plus tree-sitter-based source code analysis. Translates natural language problems into formal logic using a template-based pipeline, verifies results with mathematical certainty, and analyzes call graphs for reachability, dead code, and impact analysis.100 npm212Apache 2.0
- AlicenseBqualityFmaintenanceA lightweight bridge that wraps OpenAI's built-in tools (like web search and code interpreter) as Model Context Protocol servers, enabling their use with Claude and other MCP-compatible models.412MIT
Related Servers

perf-mcpofficial
AlicenseAqualityDmaintenanceFact-checks and fixes AI outputs by catching hallucinations, repairing broken JSON, and correcting errors before they reach users, with tools for verification, validation, and correction.437 npmMIT- AlicenseAqualityDmaintenanceRuntime quality validation for AI agent outputs. Detect hallucinations, enforce scope compliance, and score output quality — all via MCP.627 npmMIT

Kluster.ai Verify MCPofficial
AlicenseNot gradedqualityNot gradedmaintenanceEnables fact-checking of AI responses against reliable sources and validation of responses against document content to ensure accuracy and reliability.-
Arkheia Hallucinationofficial
AlicenseNot gradedqualityBmaintenanceDetect fabrication and hallucination in any LLM output. Score responses from GPT-4o, Claude, Gemini, Llama and 30+ models. Free tier included.1MIT- AlicenseNot gradedqualityDmaintenanceProvides real-time content security for large language models by identifying and intercepting risks across compliance, ethics, and safety dimensions. It enables secure input and output monitoring through a customizable policy engine using an SSE-based interface.1MIT
- AlicenseNot gradedqualityBmaintenanceAnalyzes inputs and outputs in real-time to protect against prompt injections, data leaks, secrets exposure, and phishing URLs.7 npm3MIT
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose: chat for sending validated messages, health for checking provider availability, providers for listing all supported providers, stats for session statistics, and validation_history for reviewing past validation results. There is no overlap in functionality.
All tool names follow a consistent pattern: single words or compound words with underscores (e.g., validation_history). They are all lowercase and descriptive of their function, making them easy to understand and predict.
With 5 tools, the server is well-scoped for its purpose of managing LLM chat with validation and monitoring. Each tool covers a core aspect without being overly specialized or too sparse.
The tool surface covers the full lifecycle of interacting with LLM providers: checking configuration (health, providers), sending messages (chat), reviewing performance (stats), and auditing (validation_history). No obvious gaps are present for the stated purpose.